| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
Check date integrity
Need to cut into better slices?
## sink("model/occ_baseline.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(alpha[Plant[x]])
##
## #Residuals
## discrepancy[x] <- pow(Y[x] - alpha[Plant[x]],2)
##
## #Assess Model Fit
## Ynew[x] ~ dbern(alpha[Plant[x]])
## discrepancy.new[x]<-pow(Ynew[x] - alpha[Plant[x]],2)
##
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(i in 1:Npreds){
##
## #predict value
##
## #Observation - probability of flowering
## prediction[i] ~ dbern(alpha[Ypred_plant[i]])
##
## #squared predictive error
## pred_error[i] <- pow(Ypred[i] - prediction[i],2)
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #Intercept flowering count
## alpha[j] ~ dbeta(1,1)
##
## }
##
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3416
## Unobserved stochastic nodes: 3934
## Total graph size: 19684
##
## Initializing model
## sink("model/occ_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]] + e[Plant[x],Date[x]]
##
## #Residuals
## discrepancy[x] <- pow(Y[x] - alpha[Plant[x]],2)
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-pow(Ynew[x] - alpha[Plant[x]],2)
##
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(i in 1:Npreds){
##
## #predict value
##
## #Observation - probability of flowering
## prediction[i] ~ dbern(p_new[i])
## logit(p_new[i])<-alpha[Ypred_plant[i]] + e[Ypred_plant[i],Ypred_date[i]]
##
## #squared predictive error
## pred_error[i] <- pow(Ypred[i] - prediction[i],2)
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(k in 1:Dates){
## e[1:Plants,k] ~ dmnorm(zeros[],tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #Intercept flowering count
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## #Autocorrelation priors
## gamma ~ dunif(0,20)
##
## #Strength of covariance decay
## lambda_cov = 5
## omega ~ dbeta(1,1)
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3416
## Unobserved stochastic nodes: 4216
## Total graph size: 36095
##
## Initializing model
Mean phylogenetic covariance
## sink("model/occ_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]] + e[Plant[x],Date[x]]
##
## #Residuals
## discrepancy[x] <- pow(Y[x] - alpha[Plant[x]],2)
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-pow(Ynew[x] - alpha[Plant[x]],2)
##
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(i in 1:Npreds){
##
## #predict value
##
## #Observation - probability of flowering
## prediction[i] ~ dbern(p_new[i])
## logit(p_new[i])<-alpha[Ypred_plant[i]] + e[Ypred_plant[i],Ypred_date[i]]
##
## #squared predictive error
## pred_error[i] <- pow(Ypred[i] - prediction[i],2)
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(k in 1:Dates){
## e[1:Plants,k] ~ dmnorm(zeros[],tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #Intercept flowering count
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## #Autocorrelation priors
## gamma ~ dunif(0,20)
##
## #Strength of covariance decay
## lambda_cov = 5
## omega ~ dbeta(1,1)
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3416
## Unobserved stochastic nodes: 4216
## Total graph size: 36094
##
## Initializing model
Mean phylogenetic covariance martix
## sink("model/occ_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]] + e[Plant[x],Date[x]]
##
## #Residuals
## discrepancy[x] <- pow(Y[x] - alpha[Plant[x]],2)
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-pow(Ynew[x] - alpha[Plant[x]],2)
##
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(i in 1:Npreds){
##
## #predict value
##
## #Observation - probability of flowering
## prediction[i] ~ dbern(p_new[i])
## logit(p_new[i])<-alpha[Ypred_plant[i]] + e[Ypred_plant[i],Ypred_date[i]]
##
## #squared predictive error
## pred_error[i] <- pow(Ypred[i] - prediction[i],2)
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(k in 1:Dates){
## e[1:Plants,k] ~ dmnorm(zeros[],tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #Intercept flowering count
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## #Autocorrelation priors
## gamma ~ dunif(0,20)
##
## #Strength of covariance decay
## lambda_cov = 5
## omega ~ dbeta(1,1)
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3416
## Unobserved stochastic nodes: 4216
## Total graph size: 36251
##
## Initializing model
## sink("model/occ_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]] + e[Plant[x],Date[x]]
##
## #Residuals
## discrepancy[x] <- pow(Y[x] - alpha[Plant[x]],2)
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-pow(Ynew[x] - alpha[Plant[x]],2)
##
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(i in 1:Npreds){
##
## #predict value
##
## #Observation - probability of flowering
## prediction[i] ~ dbern(p_new[i])
## logit(p_new[i])<-alpha[Ypred_plant[i]] + e[Ypred_plant[i],Ypred_date[i]]
##
## #squared predictive error
## pred_error[i] <- pow(Ypred[i] - prediction[i],2)
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(k in 1:Dates){
## e[1:Plants,k] ~ dmnorm(zeros[],tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #Intercept flowering count
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## #Autocorrelation priors
## gamma ~ dunif(0,20)
##
## #Strength of covariance decay
## lambda_cov = 5
## omega ~ dbeta(1,1)
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3416
## Unobserved stochastic nodes: 4216
## Total graph size: 36250
##
## Initializing model
The probability of occurrence.
## # A tibble: 5 x 2
## Model p
## <chr> <dbl>
## 1 baseline 0.4
## 2 phylogenetic_attraction 0.45
## 3 phylogenetic_repulsion 0.35
## 4 trait_attraction 0.6
## 5 trait_repulsion 0.3
Zoom in #Prediction